Autonomous vehicles have been developed to perform a number of functions including carrying payloads in factories and mines and providing reconnaissance information in military and space applications. A necessary feature of the navigation system on-board such vehicles is a positioning system for determining the location of the vehicle on the surface of the earth or with respect to some predefined reference frame. Conventional positioning systems include global positioning systems (GPS), inertial-based positioning systems, vision-based positioning systems, and dead-reckoning systems.
In a global positioning system, a number of satellites are placed in orbit around the Earth. The GPS satellites are designed to transmit electromagnetic signals. From these electromagnetic signals, the absolute position of any receiver at or near the surface of the Earth can be determined. Typically, signals from at least four of the GPS satellites in the field of view of an Earth receiver are used to accurately determine the receiver position.
The accuracy of the GPS is affected by the number of GPS satellites transmitting signals to which the Earth receiver is effectively responsive, the variable amplitudes of the received signals. When one or more of the GPS satellites are not functioning properly, the accuracy of the position estimates is degraded. Furthermore, if the electromagnetic signals from the satellites are blocked such as when the vehicle is operating in a deep open-pit mine or in a tunnel, the accuracy of the global positioning system is reduced.
In addition to GPS, it is known in the conventional art to use inertial reference units (IRUs) in navigation systems to obtain position estimates of vehicles. Such an IRU obtains specific-force measurments from accelerometers in a reference coordinate frame which is stabilized by gyroscopes. An IRU can be of several types, including for example, laser, mechanical, or fiber optic. In an unaided navigation system using an IRU, the specific force (corrected for the effects of the Earth's gravity) as measured by an accelerometer is integrated into a navigation mathematical equation to produce the vehicle's position and velocity.
The instrument measurements of the IRU may be specified in a different rectangular coordinate frame than the reference navigation frame, depending on the platform implementation. The most commonly used reference navigation frame for near Earth navigation is a local-level frame.
The gyroscopes and the accelerometers associated with the IRU are typically mounted directly on the vehicle body. They measure the linear and angular motion of the vehicle relative to inertial space. The motion is expressed in vehicle coordinates. Therefore, it is necessary to first compute the altitude of the vehicle to the referenced navigation frame. Then, the computed altitude is used to transform the accelerometer measurements into the reference frame.
The performance of navigation systems using IRUs is limited by errors contributed by the various constituent sensors within the IRUs. Gyroscopes drift. Accelerometers have inherent biases. Further, errors are contributed from improper scale factors and improper IRU alignment angles. Typically, the preceding errors cause inaccuracies in the estimates of vehicle positions, velocity, and altitude, which accumulate over time as a vehicle mission progresses. To some extent, the errors are dependent on user dynamics.
If a very accurate navigation system is required for a vehicle, high precision gyroscopes and accelerometers can be utilized to satisfy that need. However, such high precision equipment increase the complexity and costs of the vehicle.
Autonomous vehicle navigation systems also exist which rely on positioning based on visual sensing. For instance, vision-based positioning is used in the Martin Marietta Autonomous Land Vehicle, as described in "Obstacle Avoidance Perception Processing for the Autonomous Land Vehicle," by R. Terry Dunlay, IEEE, CH2555-1/88/0000/0912, 1988.
Some of the vision based positioning systems use fixed guide lines or markings on a factory floor, for example, to navigate from point to point. Other positioning systems involve pattern recognition by complex hardware and software. Still other systems, known as "dead-reckoning" systems, navigate by keeping track of the vehicle's position relative to a known starting point. This tracking is performed by measuring the distance the vehicle has travelled and monitoring the vehicle direction from the starting point. The preceding autonomous navigation systems suffer from numerous drawbacks and limitations. For instance, if a navigation system on a vehicle fails to recognize where the vehicle has been, or miscalculates the vehicle's starting point, then the navigation system will be unable to accurately direct the vehicle to reach its ultimate destination.
Moreover, because errors in position estimates of vehicles have a tendency to accumulate over time in conventional autonomous navigation systems, the navigation systems require frequent and time-consuming updates of actual position. Such a system for updating a dead-reckoning navigation system is disclosed in "Blanche: An Autonomous Robot Vehicle for Structured Environments", by Ingemar J. Cox, IEEE, CH2555-1/88/0000/0978, 1988. The disclosed navigation system uses a laser range-finder mounted on the vehicle in conjunction with bar-coded targets mounted in an operating area to update an odometer-based dead-reckoning system. Such a system has the disadvantage of being usable only in a two-dimensional operating area.
The present invention is directed to overcoming one or more of the problems set forth above.